Association-Rules-Based Recommender System for Personalization in Adaptive Web-Based Applications

  • Daniel Mican
  • Nicolae Tomai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6385)

Abstract

Personalization systems based upon the analysis of users’ surfing behavior imply three phases: data collection, pattern discovery and recommendation. Due to the dimension of log files and high processing time, the first two phases are achieved offline, in a batch process. In this article, we propose Wise Recommender System (WRS), an architecture for adaptive web applications. Within this framework, usage data is implicitly obtained by the data collection submodule. This allows for the extraction of usage data, online and in real time, by using a proactive approach. For the pattern discovery, we efficiently used association rule mining among both frequent and infrequent items. This is due to the fact that the pattern discovery module transactionally processes users’ sessions and uses incremental storage of rules. Finally, we will show that WRS can be easily implemented within any web application, thanks to the efficient integration of the three phases into an online transactional process.

Keywords

Adaptive web-based applications Web usage mining Recommendation systems Web personalization Association rules 

References

  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C, pp. 207–216 (1993)Google Scholar
  2. 2.
    Baraglia, R., Silvestri, F.: Dynamic personalization of web sites without user intervention. ACM Commun. 50(2), 63–67 (2007)CrossRefGoogle Scholar
  3. 3.
    Bayir, M.A., Toroslu, I.H., Cosar, A., Fidan, G.: Smart Miner: a new framework for mining large scale web usage data. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 161–170. ACM, New York (2009)Google Scholar
  4. 4.
    Catledge, L.D., Pitkow, J.E.: Characterizing browsing strategies in the World-Wide Web. Comput. Netw. ISDN Syst. 27(6), 1065–1073 (1995)CrossRefGoogle Scholar
  5. 5.
    Ceglar, A., Roddick, J.F.: Association mining. ACM Comput. Surv. 38(2) (2006)Google Scholar
  6. 6.
    Chen, M.S., Park, J.S., Yu, P.S.: Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering, 209–221 (1998)Google Scholar
  7. 7.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining World Wide Web browsing patterns. Knowledge Information Systems 1(1), 5–32 (1999)CrossRefGoogle Scholar
  8. 8.
    Ding, J., Yau, S.S.: TCOM, an innovative data structure for mining association rules among infrequent items. Comput. Math. Appl. 57(2), 290–301 (2009)CrossRefMATHGoogle Scholar
  9. 9.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on Web usage mining. ACM Commun. 43(8), 142–151 (2000)CrossRefGoogle Scholar
  10. 10.
    Perkowitz, M., Etzioni, O.: Adaptive sites: Automatically learning from user access patterns. In: Proc. of the Sixth International WWW Conference, Santa Clara, CA (1997)Google Scholar
  11. 11.
    Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis. INFORMS J. on Computing 15(2), 171–190 (2003)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Daniel Mican
    • 1
  • Nicolae Tomai
    • 1
  1. 1.Dept. of Business Information SystemsBabes-Bolyai UniversityCluj-NapocaRomania

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